Video-based Automated Incident Detection AID systems are becoming increasingly adopted to monitor roadways for traffic incidents without the need for human operators. These systems, while proficient in detecting real incidents, are not so adept in discriminating these from ordinary scene changes. Examples of such problems stem from issues such as the appearance of stationary shadows on the roadway, a vehicle's headlight beam reflecting from a road sign, vehicles pushing snow aside as they move through it, and water splashing on a roadway. As a result many false alarms are often generated, lowering the accuracy of the AID system in totality. This is the result of many AID systems not having been designed to anticipate complex lighting conditions, with changing cloud cover and/or stationary objects off camera suddenly casting shadows with a change in lighting. This problem requires rectification.
To combat this, a new method is needed to segment the video images into elements, marking the elements of the frame that contain static shadows. This would allow the sensitivity of the AID system to be lowered in these elements and result in fewer false alarms. This is a difficult task for many reasons. First, static shadows must be discriminated from moving shadows, the latter being shadows that are cast by vehicles traveling down the roadway. Secondly, the algorithm must be robust enough to work at all times from dawn until dusk, in spite of the changing lighting of the scene due to the position of the sun or cloud coverage. Thirdly, the method needs to work in a variety of camera placements such that manual parameter adjustment is not possible for each camera; hence, adaptive methodologies are introduced in lieu of manual calibration. Fourth, since the algorithm is expected to detect static shadows before the AID system does, the method must operate in real time and be relatively low in computational complexity.